What You’ll OwnDefine the architectural vision and target state for enterprise AI, data, and digital solutions.
Translate business, product, and operational requirements into end-to-end solution designs, reference architectures, and implementation guidance.
Design secure, resilient, observable, and scalable systems across public cloud, private cloud, hybrid, and sovereign environments.
Evaluate build-versus-buy decisions and technology options using clear criteria for value, risk, operability, portability, and long-term ownership.
Shape architectures for AI and generative AI workloads, including model services, retrieval-augmented generation, data pipelines, evaluation, monitoring, and lifecycle controls.
Define integration patterns for APIs, microservices, event-driven systems, data-intensive applications, identity, and enterprise platforms.
Identify opportunities to reuse platform capabilities, architecture patterns, and shared services across products and deployments.
Drive architecture reviews and technical decisions, documenting trade-offs and resolving dependencies across teams and systems.
Partner with security and governance teams to embed privacy, data sovereignty, access control, web security, auditability, and responsible AI requirements by design.
Support discovery, solution shaping, technical proposals, proof-of-value work, estimation, and delivery planning with credible architecture and risk assumptions.
Stay close to implementation, validating that delivered systems remain aligned with the intended architecture and production requirements.
Raise the technical bar through mentoring, practical design feedback, and reusable standards for engineers and solution teams.
What We’re Looking ForStrong software and systems engineering fundamentals, with the ability to reason across application, data, infrastructure, security, and operational concerns.
Experience designing and shipping production systems in complex enterprise environments.
Practical depth in at least one major cloud platform and sound judgment across cloud-native, hybrid, and constrained deployment models.
Working knowledge of APIs, microservices, containers, Kubernetes, infrastructure as code, distributed systems, and modern data architecture.
Ability to engage credibly with AI and machine learning teams and design the platform, data, integration, evaluation, and governance layers around model capabilities.
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